Welcome to class!

1. Introductions
2. Class overview
3. Getting R up and running

_{[Photo by Belinda Fewings on Unsplash]}

About Us

Alex Edwards (he/him)

Adjunct Instructor, Department of Biostatistics and Bioinformatics, RSPH

PhD candidate in Spatial Epidemiology, University of Lincoln, UK

Email: jedwa25@emory.edu

What is R?

• R is a language and environment for statistical computing and graphics developed in 1991

• R is the open source implementation of the S language, which was developed by Bell laboratories in the 70s.

• The aim of the S language, as expressed by John Chambers, is “to turn ideas into software, quickly and faithfully”

_{[source: http://www.r-project.org/, https://en.wikipedia.org/wiki/S_(programming_language), https://en.wikipedia.org/wiki/Bell_Labs)]}

What is R?

• Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand developed R

• R is both open source and open development

_{[source: http://www.r-project.org/, https://en.wikipedia.org/wiki/R_(programming_language)]}

Why R?

• Free (open source)

• High level language designed for statistical computing

• Powerful and flexible - especially for data wrangling and visualization

• Extensive add-on software (packages)

• Strong community

_{[source: https://rladies-baltimore.github.io/]}

Why not R?

• Little centralized support, relies on online community and package developers

• Annoying to update

• Slower, and more memory intensive, than the more traditional programming languages (C, Perl, Python)

_{[source -School vector created by nizovatina - www.freepik.com]}

Introductions

What do you hope to get out of the class?

Why do you want to use R?

_{[Photo by Nick Fewings on Unsplash]}

Course Website

Our Google Classroom

Materials are uploaded for your perusal. We are constantly trying to improve content!

Learning Objectives

• Understanding basic programming syntax
• Reading data into R
• Recoding and manipulating data
• Using add-on packages (more on what this is soon!)
• Making exploratory plots
• Performing basic statistical tests
• Writing R functions

Course Format

• One hour lecture with slides (possibly “Interactive”)
• 30 -45 Minute Lab/Practical experience
• 15 minute breaks at the end of each module
• One hour lunch at 1pm

Setting up RStudio Cloud

• RStudio Cloud

RStudio is an integrated development environment (IDE) that makes it easier to work with R.

Basic terms

R jargon: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf

Package - a package in R is a bundle or “package” of code (and or possibly data) that can be loaded together for easy repeated use or for sharing with others.

Packages are sort of analogous to a software application like Microsoft Word on your computer. Your operating system allows you to use it, just like having R installed (and other required packages) allows you to use packages.

Basic terms

Function - a function is a particular piece of code that allows you to do something in R. You can write your own, use functions that come directly from installing R, or use functions from additional packages.

A function might help you add numbers together, create a plot, or organize your data. More on that soon!

sum(1, 20234)

[1] 20235

Basic terms

Argument - what you pass to a function

• can be data like the number 1 or 20234

sum(1, 20234)

[1] 20235

• can be options about how you want the function to work such as digits

round(0.627, digits = 2)

[1] 0.63

round(0.627, digits = 1)

[1] 0.6

Basic terms

Object - an object is something that can be worked with in R - can be lots of different things!

• a matrix of numbers
• a plot
• a function

… many more

Variable and Sample

• Variable: something measured or counted that is a characteristic about a sample

examples: temperature, length, count, color, category

• Sample: individuals that you have data about -

examples: people, houses, viruses etc.

head(iris)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Columns and Rows

[source]

Sample = Row
Variable = Column

Data objects that looks like this is often called a data frame.

Fancier versions from the tidyverse are called tibbles (more on that soon!).

More on Functions and Packages

• When you download R, it has a “base” set of functions/packages (base R)
  • You can install additional packages for your uses from CRAN or GitHub
  • These additional packages are written by RStudio or R users/developers (like us)

Using Packages

• Not all packages available on CRAN or GitHub are trustworthy
• RStudio (the company) makes a lot of great packages
• Who wrote it? Hadley Wickham is a major authority on R (Employee and Developer at RStudio)
• How to trust an R package

(source: https://twitter.com/hadleywickham)

Tidyverse and Base R

We will mostly show you how to use tidyverse packages and functions.

This is a newer set of packages designed for data science that can make your code more intuitive as compared to the original older Base R.

Tidyverse advantages:
- consistent structure - making it easier to learn how to use different packages
- particularly good for wrangling (manipulating, cleaning, joining) data
- more flexible for visualizing data

Packages for the tidyverse are managed by a team of respected data scientists at RStudio.

See this article for more info.

Collection of R packages

We have an R package called jhur that will make sure all the packages are installed.

You can just copy and paste the below code into your console - we’ll explain what it all means in the next day or two

install.packages("remotes")
remotes::install_github("muschellij2/jhur")

Note it may take ~5-10 minutes to run.

Useful (+ mostly Free) Resources

• Tidyverse Skills for Data Science Book: https://jhudatascience.org/tidyversecourse/ (more about the tidyverse, some modeling, and machine learning)

• Tidyverse Skills for Data Science Course: https://www.coursera.org/specializations/tidyverse-data-science-r
  (same content with quizzes, can get certificate with $)

• R for Data Science: http://r4ds.had.co.nz/
  (great general information)

• R basics by Rafael A. Irizarry: https://rafalab.github.io/dsbook/r-basics.html (great general information)

• Open Case Studies: https://www.opencasestudies.org/
  (resource for specific public health cases with statistical implementation and interpretation)

• Dataquest: https://www.dataquest.io/
  (general interactive resource)

Useful (+ mostly Free) Resources

Need help?

• Various “Cheat Sheets”: https://www.rstudio.com/resources/cheatsheets/

• R reference card: http://cran.r-project.org/doc/contrib/Short-refcard.pdf

• R jargon: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf

• R vs Stata: https://link.springer.com/content/pdf/bbm%3A978-1-4419-1318-0%2F1.pdf

• R terminology: https://cran.r-project.org/doc/manuals/r-release/R-lang.pdf

Summary

• R is a powerful data visualization and analysis software language
• We will focus on packages (code shared among people) of the tidyverse, which helps make R more intuitive.
• We will also talk a bit about base R because some resources online and R users will use this.
• Functions perform specific tasks in R and are found within packages.
• Arguments within functions specify how a function is to be performed.
• Materials will be updated frequently as we improve it.
• Class surveys are available on CoursePlus so you can provide feedback!
• Lots of resources can be found on the website.

🏠 Class Website

Website tour!

🏠 Class Website